{"id":{"repo_id":"duquesne","oai_identifier":"oai:dsc.duq.edu:etd-1589"},"canonical_url":"https://search.dev.ndltd.org/etd/duquesne/oai:dsc.duq.edu:etd-1589","repository":{"repo_id":"duquesne","name":"Duquesne","base_url":"https://dsc.duq.edu/do/oai/"},"display":{"title":"Characterization of an Evolving Serotonin Transporter Computational Model","abstract":"A major obstacle for developing new antidepressants has been limited knowledge of the structure and function of a central target, the serotonin transporter (SERT). Established SERT inhibitors (SSRIs) were docked to an in silico SERT model to identify likely binding pocket amino acid residues. When mutated singly, no one of five implicated residues was critical for high affinity in vitro binding of SSRIs or cocaine. The in silico SERT model was used in ligand virtual screening (VS) of a small molecule structural library. Selected VS \"hit\" compounds were procured and tested in vitro; encouragingly, two compounds with novel structural scaffolds bound SERT with modest affinity. The combination of computational modeling, site-directed mutagenesis and pharmacologic characterization can accelerate binding site elucidation and the search for novel lead compounds. Such compounds may be tailored for improved serotonin receptor selectivity and reduced affinity for extraneous targets, providing superior antidepressants with fewer adverse effects.","abstract_html":"A major obstacle for developing new antidepressants has been limited knowledge of the structure and function of a central target, the serotonin transporter (SERT). Established SERT inhibitors (SSRIs) were docked to an in silico SERT model to identify likely binding pocket amino acid residues. When mutated singly, no one of five implicated residues was critical for high affinity in vitro binding of SSRIs or cocaine. The in silico SERT model was used in ligand virtual screening (VS) of a small molecule structural library. Selected VS "hit" compounds were procured and tested in vitro; encouragingly, two compounds with novel structural scaffolds bound SERT with modest affinity. The combination of computational modeling, site-directed mutagenesis and pharmacologic characterization can accelerate binding site elucidation and the search for novel lead compounds. Such compounds may be tailored for improved serotonin receptor selectivity and reduced affinity for extraneous targets, providing superior antidepressants with fewer adverse effects.","abstract_has_math":false,"creators":["Geffert, Laura Marie"],"institution":null,"degree_name":"MS","degree_level":"Immediate Access","degree_discipline":"Pharmacology","degree_department":null,"school":null,"contributors":["Christopher Surratt","Jeffry Madura","Jane Cavanaugh"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013-01-01T08:00:00Z","date_published":"2013-01-01T08:00:00Z","updated_at":"2026-07-24T02:09:46Z","subjects":["Antidepressant","Binding","Computational model","Mutagenesis","Serotonin","SERT"],"languages":["English"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://dsc.duq.edu/etd/573","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Christopher Surratt","Jeffry Madura","Jane Cavanaugh"]},{"key":"dc:creator","label":"Author","values":["Geffert, Laura Marie"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2018-03-28T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Pharmacology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Immediate Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Antidepressant","Binding","Computational model","Mutagenesis","Serotonin","SERT"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://dsc.duq.edu/etd/573"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["A major obstacle for developing new antidepressants has been limited knowledge of the structure and function of a central target, the serotonin transporter (SERT). Established SERT inhibitors (SSRIs) were docked to an in silico SERT model to identify likely binding pocket amino acid residues. When mutated singly, no one of five implicated residues was critical for high affinity in vitro binding of SSRIs or cocaine. The in silico SERT model was used in ligand virtual screening (VS) of a small molecule structural library. Selected VS \"hit\" compounds were procured and tested in vitro; encouragingly, two compounds with novel structural scaffolds bound SERT with modest affinity. The combination of computational modeling, site-directed mutagenesis and pharmacologic characterization can accelerate binding site elucidation and the search for novel lead compounds. Such compounds may be tailored for improved serotonin receptor selectivity and reduced affinity for extraneous targets, providing superior antidepressants with fewer adverse effects."]},{"key":"dc:title","label":"Title","values":["Characterization of an Evolving Serotonin Transporter Computational Model"]}]}],"canonical_facts":{"dc:contributor":["Christopher Surratt","Jeffry Madura","Jane Cavanaugh"],"dc:creator":["Geffert, Laura Marie"],"dc:date.available":["2018-03-28T07:00:00Z"],"dc:description.abstract":["A major obstacle for developing new antidepressants has been limited knowledge of the structure and function of a central target, the serotonin transporter (SERT). Established SERT inhibitors (SSRIs) were docked to an in silico SERT model to identify likely binding pocket amino acid residues. When mutated singly, no one of five implicated residues was critical for high affinity in vitro binding of SSRIs or cocaine. The in silico SERT model was used in ligand virtual screening (VS) of a small molecule structural library. Selected VS \"hit\" compounds were procured and tested in vitro; encouragingly, two compounds with novel structural scaffolds bound SERT with modest affinity. The combination of computational modeling, site-directed mutagenesis and pharmacologic characterization can accelerate binding site elucidation and the search for novel lead compounds. Such compounds may be tailored for improved serotonin receptor selectivity and reduced affinity for extraneous targets, providing superior antidepressants with fewer adverse effects."],"dc:identifier":["https://dsc.duq.edu/etd/573"],"dc:language":["English"],"dc:subject":["Antidepressant","Binding","Computational model","Mutagenesis","Serotonin","SERT"],"dc:title":["Characterization of an Evolving Serotonin Transporter Computational Model"],"thesis:degree_discipline":["Pharmacology"],"thesis:degree_level":["Immediate Access"],"thesis:degree_name":["MS"]},"updated_at":"2026-07-24T02:09:46Z"}